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BACKGROUND: Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations. OBJECTIVE: We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting. METHODS: Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA. RESULTS: All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001). CONCLUSIONS: Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.3390/clockssleep6010010.
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Frecuencia Cardíaca , Frecuencia Respiratoria , Humanos , Anciano , Frecuencia Cardíaca/fisiología , Masculino , Femenino , Anciano de 80 o más Años , Frecuencia Respiratoria/fisiología , Monitoreo Fisiológico/métodos , Monitoreo Fisiológico/instrumentación , Polisomnografía/métodos , Polisomnografía/instrumentación , Evaluación de la Tecnología Biomédica/métodos , Salud DigitalRESUMEN
Analyses of gene-expression dynamics in research on circadian rhythms and sleep homeostasis often describe these two processes using separate models. Rhythmically expressed genes are, however, likely to be influenced by both processes. We implemented a driven, damped harmonic oscillator model to estimate the contribution of circadian- and sleep-wake-driven influences on gene expression. The model reliably captured a wide range of dynamics in cortex, liver, and blood transcriptomes taken from mice and humans under various experimental conditions. Sleep-wake-driven factors outweighed circadian factors in driving gene expression in the cortex, whereas the opposite was observed in the liver and blood. Because of tissue- and gene-specific responses, sleep deprivation led to a long-lasting intra- and inter-tissue desynchronization. The model showed that recovery sleep contributed to these long-lasting changes. The results demonstrate that the analyses of the daily rhythms in gene expression must take the complex interactions between circadian and sleep-wake influences into account. A record of this paper's transparent peer review process is included in the supplemental information.
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Ritmo Circadiano , Sueño , Vigilia , Ritmo Circadiano/genética , Ritmo Circadiano/fisiología , Animales , Humanos , Sueño/genética , Sueño/fisiología , Ratones , Vigilia/fisiología , Vigilia/genética , Regulación de la Expresión Génica/genética , Hígado/metabolismo , Transcriptoma/genética , Privación de Sueño/genética , Privación de Sueño/fisiopatología , Masculino , Homeostasis/genéticaRESUMEN
[This corrects the article DOI: 10.1016/j.isci.2024.109331.].
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Alpha oscillations play a vital role in managing the brain's resources, inhibiting neural activity as a function of their phase and amplitude, and are changed in many brain disorders. Developing minimally invasive tools to modulate alpha activity and identifying the parameters that determine its response to exogenous modulators is essential for the implementation of focussed interventions. We introduce Alpha Closed-Loop Auditory Stimulation (αCLAS) as an EEG-based method to modulate and investigate these brain rhythms in humans with specificity and selectivity, using targeted auditory stimulation. Across a series of independent experiments, we demonstrate that αCLAS alters alpha power, frequency, and connectivity in a phase, amplitude, and topography-dependent manner. Using single-pulse-αCLAS, we show that the effects of auditory stimuli on alpha oscillations can be explained within the theoretical framework of oscillator theory and a phase-reset mechanism. Finally, we demonstrate the functional relevance of our approach by showing that αCLAS can interfere with sleep onset dynamics in a phase-dependent manner.
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Estimulación Acústica , Ritmo alfa , Electroencefalografía , Humanos , Estimulación Acústica/métodos , Masculino , Adulto , Ritmo alfa/fisiología , Electroencefalografía/métodos , Femenino , Adulto Joven , Sueño/fisiología , Encéfalo/fisiologíaRESUMEN
OBJECTIVE: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. METHODS AND PROCEDURES: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. RESULTS: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. CONCLUSION: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. CLINICAL IMPACT: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.
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Electroencefalografía , Cuero Cabelludo , Humanos , Electroencefalografía/métodos , Anciano , Cuero Cabelludo/fisiología , Anciano de 80 o más Años , Masculino , Femenino , Sueño/fisiología , Procesamiento de Señales Asistido por Computador , Oído/fisiología , Aprendizaje Automático , Polisomnografía/métodosRESUMEN
Physiological and molecular processes including the transcriptome change across the 24-h day, driven by molecular circadian clocks and behavioral and systemic factors. It is not known how the temporal organization of the human transcriptome responds to a long-lasting challenge. This may, however, provide insights into adaptation, disease, and recovery. We investigated the human 24-h time series transcriptome in 20 individuals during a 90-day constant bed rest protocol. We show that the protocol affected 91% of the transcriptome with 76% of the transcriptome still affected after 10 days of recovery. Dimensionality-reduction approaches revealed that many affected transcripts were associated with mRNA translation and immune function. The number, amplitude, and phase of rhythmic transcripts, including clock genes, varied significantly across the challenge. These findings of long-lasting changes in the temporal organization of the transcriptome have implications for understanding the mechanisms underlying health consequences of conditions such as microgravity and bed rest.
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Twenty-four-hour rhythms in physiology and behaviour are shaped by circadian clocks, environmental rhythms, and feedback of behavioural rhythms onto physiology. In space, 24 h signals such as those associated with the light-dark cycle and changes in posture, are weaker, potentially reducing the robustness of rhythms. Head down tilt (HDT) bed rest is commonly used to simulate effects of microgravity but how HDT affects rhythms in physiology has not been extensively investigated. Here we report effects of -6° HDT during a 90-day protocol on 24 h rhythmicity in 20 men. During HDT, amplitude of light, motor activity, and wrist-temperature rhythms were reduced, evening melatonin was elevated, while cortisol was not affected during HDT, but was higher in the morning during recovery when compared to last session of HDT. During recovery from HDT, time in Slow-Wave Sleep increased. EEG activity in alpha and beta frequencies increased during NREM and REM sleep. These results highlight the profound effects of head-down-tilt-bed-rest on 24 h rhythmicity.
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Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.
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Sleep disturbance is a prevalent and disabling comorbidity in Parkinson's disease (PD). We performed multi-night (n = 57) at-home intracranial recordings from electrocorticography and subcortical electrodes using sensing-enabled Deep Brain Stimulation (DBS), paired with portable polysomnography in four PD participants and one with cervical dystonia (clinical trial: NCT03582891). Cortico-basal activity in delta increased and in beta decreased during NREM (N2 + N3) versus wakefulness in PD. DBS caused further elevation in cortical delta and decrease in alpha and low-beta compared to DBS OFF state. Our primary outcome demonstrated an inverse interaction between subcortical beta and cortical slow-wave during NREM. Our secondary outcome revealed subcortical beta increases prior to spontaneous awakenings in PD. We classified NREM vs. wakefulness with high accuracy in both traditional (30 s: 92.6 ± 1.7%) and rapid (5 s: 88.3 ± 2.1%) data epochs of intracranial signals. Our findings elucidate sleep neurophysiology and impacts of DBS on sleep in PD informing adaptive DBS for sleep dysfunction.
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Enfermedad de Parkinson , Núcleo Subtalámico , Humanos , Enfermedad de Parkinson/terapia , Sueño/fisiología , Polisomnografía , ElectrocorticografíaRESUMEN
Accurate assessment of the intrinsic period of the human circadian pacemaker is essential for a quantitative understanding of how our circadian rhythms are synchronized to exposure to natural and man-made light-dark (LD) cycles. The gold standard method for assessing intrinsic period in humans is forced desynchrony (FD) which assumes that the confounding effect of lights-on assessment of intrinsic period is removed by scheduling sleep-wake and associated dim LD cycles to periods outside the range of entrainment of the circadian pacemaker. However, the observation that the mean period of free-running blind people is longer than the mean period of sighted people assessed by FD (24.50 ± 0.17 h vs 24.15 ± 0.20 h, p <0.001) appears inconsistent with this assertion. Here, we present a mathematical analysis using a simple parametric model of the circadian pacemaker with a sinusoidal velocity response curve (VRC) describing the effect of light on the speed of the oscillator. The analysis shows that the shorter period in FD may be explained by exquisite sensitivity of the human circadian pacemaker to low light intensities and a VRC with a larger advance region than delay region. The main implication of this analysis, which generates new and testable predictions, is that current quantitative models for predicting how light exposure affects entrainment of the human circadian system may not accurately capture the effect of dim light. The mathematical analysis generates new predictions which can be tested in laboratory experiments. These findings have implications for managing healthy entrainment of human circadian clocks in societies with abundant access to light sources with powerful biological effects.
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Relojes Circadianos , Ritmo Circadiano , Humanos , Ritmo Circadiano/fisiología , Temperatura Corporal/fisiología , Sueño/fisiología , Luz , FotofobiaRESUMEN
Sleep is considered essential for the brain and body. A predominant concept is that sleep is regulated by circadian rhythmicity and sleep homeostasis, processes that were posited to be functionally and mechanistically separate. Here we review and re-evaluate this concept and its assumptions using findings from recent human and rodent studies. Alterations in genes that are central to circadian rhythmicity affect not only sleep timing but also putative markers of sleep homeostasis such as electroencephalogram slow-wave activity (SWA). Perturbations of sleep change the rhythmicity in the expression of core clock genes in tissues outside the central clock. The dynamics of recovery from sleep loss vary across sleep variables: SWA and immediate early genes show an early response, but the recovery of non-rapid eye movement and rapid eye movement sleep follows slower time courses. Changes in the expression of many genes in response to sleep perturbations outlast the effects on SWA and time spent asleep. These findings are difficult to reconcile with the notion that circadian- and sleep-wake-driven processes are mutually independent and that the dynamics of sleep homeostasis are reflected in a single variable. Further understanding of how both sleep and circadian rhythmicity contribute to the homeostasis of essential physiological variables may benefit from the assessment of multiple sleep and molecular variables over longer time scales.
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Sueño , Vigilia , Humanos , Vigilia/fisiología , Sueño/fisiología , Ritmo Circadiano/fisiología , Sueño REM/genética , Electroencefalografía , Homeostasis/fisiologíaRESUMEN
Sleep disorders are a prevalent problem among older adults, yet obtaining an accurate and reliable assessment of sleep quality can be challenging. Traditional polysomnography (PSG) is the gold standard for sleep staging, but is obtrusive, expensive, and requires expert assistance. To this end, we propose a minimally invasive single-channel single ear-EEG automatic sleep staging method for older adults. The method employs features from the frequency, time, and structural complexity domains, which provide a robust classification of sleep stages from a standardised viscoelastic earpiece. Our method is verified on a dataset of older adults and achieves a kappa value of at least 0.61, indicating substantial agreement. This paves the way for a non-invasive, cost-effective, and portable alternative to traditional PSG for sleep staging.
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Trastornos del Sueño-Vigilia , Sueño , Humanos , Anciano , Polisomnografía/métodos , Fases del Sueño , Electroencefalografía/métodosRESUMEN
[This corrects the article DOI: 10.2196/46338.].
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Sleep timing varies between individuals and can be altered in mental and physical health conditions. Sleep and circadian sleep phenotypes, including circadian rhythm sleep-wake disorders, may be driven by endogenous physiological processes, exogeneous environmental light exposure along with social constraints and behavioural factors. Identifying the relative contributions of these driving factors to different phenotypes is essential for the design of personalised interventions. The timing of the human sleep-wake cycle has been modelled as an interaction of a relaxation oscillator (the sleep homeostat), a stable limit cycle oscillator with a near 24-hour period (the circadian process), man-made light exposure and the natural light-dark cycle generated by the Earth's rotation. However, these models have rarely been used to quantitatively describe sleep at the individual level. Here, we present a new Homeostatic-Circadian-Light model (HCL) which is simpler, more transparent and more computationally efficient than other available models and is designed to run using longitudinal sleep and light exposure data from wearable sensors. We carry out a systematic sensitivity analysis for all model parameters and discuss parameter identifiability. We demonstrate that individual sleep phenotypes in each of 34 older participants (65-83y) can be described by feeding individual participant light exposure patterns into the model and fitting two parameters that capture individual average sleep duration and timing. The fitted parameters describe endogenous drivers of sleep phenotypes. We then quantify exogenous drivers using a novel metric which encodes the circadian phase dependence of the response to light. Combining endogenous and exogeneous drivers better explains individual mean mid-sleep (adjusted R-squared 0.64) than either driver on its own (adjusted R-squared 0.08 and 0.17 respectively). Critically, our model and analysis highlights that different people exhibiting the same sleep phenotype may have different driving factors and opens the door to personalised interventions to regularize sleep-wake timing that are readily implementable with current digital health technology.
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Ritmo Circadiano , Sueño , Humanos , Sueño/fisiología , Ritmo Circadiano/fisiología , Fenotipo , Homeostasis , Modelos TeóricosRESUMEN
Background: Sleep disturbance is a prevalent and highly disabling comorbidity in individuals with Parkinson's disease (PD) that leads to worsening of daytime symptoms, reduced quality of life and accelerated disease progression. Objectives: We aimed to record naturalistic overnight cortico-basal neural activity in people with PD, in order to determine the neurophysiology of spontaneous awakenings and slow wave suppression in non-rapid eye movement (NREM) sleep, towards the development of novel sleep-targeted neurostimulation therapies. Methods: Multi-night (n=58) intracranial recordings were performed at-home, from chronic electrocorticography and subcortical electrodes, with sensing-enabled Deep Brain Stimulation (DBS), paired with portable polysomnography. Four participants with PD and one participant with cervical dystonia were evaluated to determine the neural structures, signals and functional connectivity modulated during NREM sleep and prior to spontaneous awakenings. Intracranial recordings were performed both ON and OFF DBS to evaluate the impact of stimulation. Sleep staging was then classified with machine-learning models using intracranial cortico-basal signals on classical (30 s) and rapid (5 s) timescales. Results: We demonstrate an increase in cortico-basal slow wave delta (1-4 Hz) activity and a decrease in beta (13-31 Hz) activity during NREM (N2 and N3) versus wakefulness in PD. Cortical-basal ganglia coherence was also found to be higher in the delta range and lower in the beta range during NREM. DBS stimulation resulted in a further elevation in cortical delta and a decrease in alpha (8-13 Hz) and low beta (13-15 Hz) power compared to the OFF stimulation state. Within NREM sleep, we observed a strong inverse interaction between subcortical beta and cortical slow wave activity and found that subcortical beta increases prior to spontaneous awakenings at high-temporal resolution (5s). Our machine-learning models trained on intracranial cortical or subcortical power features achieved high accuracy in both traditional (30s) and rapid (5s) time windows for NREM vs. wakefulness classification (30s: 92.6±1.7%; 5s: 88.3±2.1%). Conclusions: Chronic, multi-night recordings in PD reveal increased cortico-basal slow wave, decreased beta activity, and changes in functional connectivity in NREM vs wakefulness, effects that are enhanced in the presence of DBS. Within NREM, subcortical beta and cortical delta are strongly inversely correlated and subcortical beta power increases prior to spontaneous awakenings. Our findings elucidate the network-level neurophysiology of sleep dysfunction in PD and the mechanistic impact of conventional DBS. Additionally, through accurate machine-learning classification of spontaneous awakenings, this study also provides a foundation for future personalized adaptive DBS therapies for sleep dysfunction in PD.
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BACKGROUND: Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people. OBJECTIVE: This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population. METHODS: Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard. RESULTS: The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9% (SD 10.4%; range 52.27%-92.60%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: <6 min) but overestimated by the undermattress devices (bias: >16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r2=0.60; P<.01) with electroencephalography slow wave activity (0.75-4.5 Hz) derived from PSG, whereas for the undermattress devices, this correlation was not significant (WSA: r2=0.0096, P=.58; Emfit: r2=0.11, P=.21). CONCLUSIONS: These CSTs overestimated the TST, and sleep stage prediction was unsatisfactory in this group of older people in whom SEFF was relatively low. Although it was previously shown that CSTs provide useful information on bed occupancy, which may be useful for particular use cases, the performance of these CSTs with respect to the TST and sleep stage estimation requires improvement before they can serve as an alternative to PSG in estimating most sleep variables in older individuals.
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Actigrafía , Sueño , Masculino , Femenino , Humanos , Anciano , Polisomnografía , Duración del Sueño , Fases del SueñoRESUMEN
STUDY OBJECTIVES: To compare the 24-hour sleep assessment capabilities of two contactless sleep technologies (CSTs) to actigraphy in community-dwelling older adults. METHODS: We collected 7-14 days of data at home from 35 older adults (age: 65-83), some with medical conditions, using Withings Sleep Analyser (WSA, n = 29), Emfit QS (Emfit, n = 17), a standard actigraphy device (Actiwatch Spectrum [AWS, n = 34]), and a sleep diary (n = 35). We compared nocturnal and daytime sleep measures estimated by the CSTs and actigraphy without sleep diary information (AWS-A) against sleep-diary-assisted actigraphy (AWS|SD). RESULTS: Compared to sleep diary, both CSTs accurately determined the timing of nocturnal sleep (intraclass correlation [ICC]: going to bed, getting out of bed, time in bed >0.75), whereas the accuracy of AWS-A was much lower. Compared to AWS|SD, the CSTs overestimated nocturnal total sleep time (WSA: +92.71 ± 81.16 minutes; Emfit: +101.47 ± 75.95 minutes) as did AWS-A (+46.95 ± 67.26 minutes). The CSTs overestimated sleep efficiency (WSA: +9.19% ± 14.26%; Emfit: +9.41% ± 11.05%), whereas AWS-A estimate (-2.38% ± 10.06%) was accurate. About 65% (n = 23) of participants reported daytime naps either in bed or elsewhere. About 90% in-bed nap periods were accurately determined by WSA while Emfit was less accurate. All three devices estimated 24-hour sleep duration with an error of ≈10% compared to the sleep diary. CONCLUSIONS: CSTs accurately capture the timing of in-bed nocturnal sleep periods without the need for sleep diary information. However, improvements are needed in assessing parameters such as total sleep time, sleep efficiency, and naps before these CSTs can be fully utilized in field settings.